





'''simple docstring'''






import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCAmelCase									=   """<<<<<<< This should probably be modified because it mentions: """

lowerCAmelCase									=   """=======
>>>>>>>
"""

lowerCAmelCase									=   [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowerCAmelCase									=   [
    # (pattern, replacement)
    # Order is important here for some replacements
    (R"""tfds\.core""", R"""datasets"""),
    (R"""tf\.io\.gfile\.GFile""", R"""open"""),
    (R"""tf\.([\w\d]+)""", R"""datasets.Value('\1')"""),
    (R"""tfds\.features\.Text\(\)""", R"""datasets.Value('string')"""),
    (R"""tfds\.features\.Text\(""", R"""datasets.Value('string'),"""),
    (R"""features\s*=\s*tfds.features.FeaturesDict\(""", R"""features=datasets.Features("""),
    (R"""tfds\.features\.FeaturesDict\(""", R"""dict("""),
    (R"""The TensorFlow Datasets Authors""", R"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
    (R"""tfds\.""", R"""datasets."""),
    (R"""dl_manager\.manual_dir""", R"""self.config.data_dir"""),
    (R"""self\.builder_config""", R"""self.config"""),
]






def   __A    (				a_    :	Namespace						):
		return ConvertCommand(args.tfds_path   ,args.datasets_directory						)

class 		lowerCamelCase	(   _A							):



	@staticmethod
	def 	_lowerCamelCase     ( a_						):
			lowerCAmelCase  :     Union[str, Any]       						= parser.add_parser(
			    "convert"		,   help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."		,   )
			train_parser.add_argument(
			    "--tfds_path"		,   type=a_		,   required=a_		,   help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."		,   )
			train_parser.add_argument(
			    "--datasets_directory"		,   type=a_		,   required=a_		,   help="Path to the HuggingFace Datasets folder."						)
			train_parser.set_defaults(func=a_						)



	def __init__( self		,   a_		,   a_		,   *a_						):
			lowerCAmelCase  :     List[Any]       						= get_logger("datasets-cli/converting"						)

			lowerCAmelCase  :     Optional[int]       						= tfds_path
			lowerCAmelCase  :     List[Any]       						= datasets_directory





	def 	_lowerCamelCase     ( self						):
			if os.path.isdir(self._tfds_path						):
					lowerCAmelCase  :     Optional[Any]       						= os.path.abspath(self._tfds_path						)
			elif os.path.isfile(self._tfds_path						):
					lowerCAmelCase  :     Tuple       						= os.path.dirname(self._tfds_path						)
			else:
					raise ValueError("--tfds_path is neither a directory nor a file. Please check path."						)

			lowerCAmelCase  :     Any       						= os.path.abspath(self._datasets_directory						)

			self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''						)

			lowerCAmelCase  :     int       						= []
			lowerCAmelCase  :     Tuple       						= []
			lowerCAmelCase  :     Optional[int]       						= {}

			if os.path.isdir(self._tfds_path						):
					lowerCAmelCase  :     Dict       						= os.listdir(a_						)
			else:
					lowerCAmelCase  :     int       						= [os.path.basename(self._tfds_path						)]

			for f_name in file_names:
					self._logger.info(F'''Looking at file {f_name}'''						)
					lowerCAmelCase  :     str       						= os.path.join(a_		,   a_						)
					lowerCAmelCase  :     List[str]       						= os.path.join(a_		,   a_						)

					if not os.path.isfile(a_						) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
							self._logger.info("Skipping file"						)
							continue

					with open(a_		,   encoding="utf-8"						) as f:
							lowerCAmelCase  :     List[str]       						= f.readlines()

					lowerCAmelCase  :     Tuple       						= []
					lowerCAmelCase  :     Optional[int]       						= False
					lowerCAmelCase  :     Tuple       						= False
					lowerCAmelCase  :     List[str]       						= []
					for line in lines:
							lowerCAmelCase  :     Optional[Any]       						= line

							# Convert imports
							if "import tensorflow.compat.v2 as tf" in out_line:
									continue
							elif "@tfds.core" in out_line:
									continue
							elif "builder=self" in out_line:
									continue
							elif "import tensorflow_datasets.public_api as tfds" in out_line:
									lowerCAmelCase  :     Dict       						= "import datasets\n"
							elif "import tensorflow" in out_line:
									# order is important here
									lowerCAmelCase  :     int       						= ""
									continue
							elif "from absl import logging" in out_line:
									lowerCAmelCase  :     Tuple       						= "from datasets import logging\n"
							elif "getLogger" in out_line:
									lowerCAmelCase  :     Tuple       						= out_line.replace("getLogger"		,   "get_logger"						)
							elif any(expression in out_line for expression in TO_HIGHLIGHT						):
									lowerCAmelCase  :     List[Any]       						= True
									lowerCAmelCase  :     str       						= list(filter(lambda a_						: e in out_line		,   a_						)						)
									out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(a_						) + "\n"						)
									out_lines.append(a_						)
									out_lines.append(a_						)
									continue
							else:
									for pattern, replacement in TO_CONVERT:
											lowerCAmelCase  :     Optional[Any]       						= re.sub(a_		,   a_		,   a_						)

                # Take care of saving utilities (to later move them together with main script)
							if "tensorflow_datasets" in out_line:
									lowerCAmelCase  :     int       						= re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)"		,   a_						)
									tfds_imports.extend(imp.strip() for imp in match.group(1						).split(","						)						)
									lowerCAmelCase  :     Optional[int]       						= "from . import " + match.group(1						)

							# Check we have not forget anything
							if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
									raise ValueError(F'''Error converting {out_line.strip()}'''						)

							if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
									lowerCAmelCase  :     Optional[Any]       						= True
							out_lines.append(a_						)

					if is_builder or "wmt" in f_name:
							# We create a new directory for each dataset
							lowerCAmelCase  :     Tuple       						= f_name.replace(".py"		,   ""						)
							lowerCAmelCase  :     Dict       						= os.path.join(a_		,   a_						)
							lowerCAmelCase  :     Dict       						= os.path.join(a_		,   a_						)
							os.makedirs(a_		,   exist_ok=a_						)
							self._logger.info(F'''Adding directory {output_dir}'''						)
							imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}						)
					else:
							# Utilities will be moved at the end
							utils_files.append(a_						)

					if needs_manual_update:
							with_manual_update.append(a_						)

					with open(a_		,   "w"		,   encoding="utf-8"						) as f:
							f.writelines(a_						)
					self._logger.info(F'''Converted in {output_file}'''						)

			for utils_file in utils_files:
					try:
							lowerCAmelCase  :     Optional[Any]       						= os.path.basename(a_						)
							lowerCAmelCase  :     Union[str, Any]       						= imports_to_builder_map[f_name.replace(".py"		,   ""						)]
							self._logger.info(F'''Moving {dest_folder} to {utils_file}'''						)
							shutil.copy(a_		,   a_						)
					except KeyError:
							self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.'''						)

			if with_manual_update:
					for file_path in with_manual_update:
							self._logger.warning(
							    F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''						)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



